# Flash-dLLM：面向扩散大语言模型的 IO 感知 KV 缓存与并行解码加速框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-22 08:00
- AIHOT 分数：43
- AIHOT 链接：https://aihot.news/items/cmudj6uwj0coprogguqqtybol
- 原文链接：https://arxiv.org/abs/2609.26796

## AI 摘要

Flash-dLLM 是一个免训练的扩散大语言模型（dLLM）推理加速框架，通过 IO 感知的融合 KV 缓存内核和 KV 缓存驱动的 draft-and-verify 解码策略，让 dLLM 自身同时充当草稿模型与验证器。

## 正文

Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text generation. However, their practical deployment remains limited by inefficient inference, largely due to the absence of effective Key-Value (KV) caching and scalable parallel decoding mechanisms. Existing acceleration methods typically study KV caching and parallel decoding in isolation, overlooking the I/O bottlenecks that arise when cache reuse and parallel token verification are jointly applied.

In this work, we introduce Flash-dLLM, a training-free inference acceleration framework for fast and memory-efficient dLLMs. Flash-dLLM first identifies GPU memory I/O as a dominant bottleneck in KV-cache-enabled dLLM inference and addresses it with an I/O-aware fused KV-cache kernel that reduces redundant memory movement. Building on this optimized cache mechanism, Flash-dLLM further proposes an efficient KV-cache-driven draft-and-verify decoding strategy, where the dLLM itself serves as both drafter and verifier without requiring an auxiliary model.

This unified design enables faster decoding while preserving generation quality and improving scalability to longer sequences and larger batch size. Extensive experiments on mathematical reasoning and code-generation benchmarks demonstrate that Flash-dLLM consistently outperforms existing state-of-the-art dLLM acceleration methods in both inference speed and memory efficiency. In particular, it achieves 5.1times and 11.0times speedups over prior strongest baseline Elastic-Cache on GSM8K and HumanEval, respectively.